day20sept18a.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('/dls_sw/apps/scisoftpy/2.7')\n",
    "sys.path.append('/dls_sw/i16/software/python')\n",
    "from dlstools import *\n",
    "from dlstools.pdnx import *\n",
    "from matplotlib.pyplot import *\n",
    "from dlstools.quickfit import *\n",
    "from dlstools.dirty_fit import fit\n",
    "from mpl_toolkits.mplot3d import Axes3D \n",
    "#%matplotlib inline\n",
    "%matplotlib notebook\n",
    "#pip install lmfit\n",
    "\n",
    "p='/dls/i16/data/2020/cm26473-1/%i.nxs'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "figure()\n",
    "for scan in range(820926, 821068):\n",
    "    n = pdnx(p % scan)\n",
    "\n",
    "    brl = list(n.bragg)\n",
    "    idx = np.array(range(len(brl)))\n",
    "    bstep = (brl[-1] - brl[0])/(len(brl) -1)\n",
    "    bcalc = brl[0] + idx * bstep\n",
    "    \n",
    "    plot(n.en, n.bragg - bcalc, '.')\n",
    "    #plot(n.bragg - bcalc, '.')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
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       "            fig.send_message(\"refresh\", {});\n",
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       "\n",
       "    this.imageObj.onload = function() {\n",
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       "                // Full images could contain transparency (where diff images\n",
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       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n",
      "/dls_sw/i16/software/python/dlstools/pdnx.py:121: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  setattr(self,'nx',_nx)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "figure()\n",
    "for scan in range(820926, 821068):\n",
    "    n = pdnx(p % scan)\n",
    "    plot(n.en, n.bragg, '.')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.300000722784901"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#n['en_calc'] = (n['en'].max() -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "brl = list(n.bragg)\n",
    "idx = np.array(range(len(brl)))\n",
    "bstep = (brl[-1] - brl[0])/len(brl)\n",
    "bcalc = brl[0] + idx * bstep\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   0,    1,    2, ...,  998,  999, 1000])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-21.62908   , -21.62952325, -21.62996649, ..., -22.07144026,\n",
       "       -22.07188351, -22.07232675])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bcalc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "list(n.bragg)"
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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